Classification of radar signals using time-frequency transforms and fuzzy clustering

Mingqiu, Ren; Cai, Jianguo; Zhu, Yuanqing

doi:10.1109/icmmt.2010.5525213

Cited by 9 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has also been investigated by radar researchers as a very useful tool for radar signal detection, analysis and recognition [5, 8-10, 12, 13]. Such an analysis provides also new identification tool for Electronic Warfare Systems [5] and for classification of the modulation type of the intercepted radar signals [14,15]. This paper presents an application of the TFA for solving the radar signal recognition problem.…”

Section: Introductionmentioning

confidence: 98%

Radar signal recognition based on time-frequency representations and multidimensional probability density function estimator

Konopko

Grishin

Janczak

2015

2015 Signal Processing Symposium (SPSympo)

View full text Add to dashboard Cite

A radar signal recognition can be accomplished by exploiting the particular features of a radar signal observed in presence of noise. The features are the result of slight radar component variations and acts as an individual signature. The paper describes radar signal recognition algorithm based on time frequency analysis, noise reduction and statistical classification procedures. The proposed method is based on the Wigner-Ville Distribution with using a two-dimensional denoising filter which is followed by a probability density function estimator which extracts the features vector. Finally the statistical classifier is used for the radar signal recognition. The numerical simulation results for the P4-coded signals are presented.

show abstract

Section: Introductionmentioning

confidence: 98%

Radar signal recognition based on time-frequency representations and multidimensional probability density function estimator

Konopko

Grishin

Janczak

2015

2015 Signal Processing Symposium (SPSympo)

View full text Add to dashboard Cite

show abstract

“…However, the mapping from one-dimensional signal to two-dimensional representation can introduce a large dimensionality problem. Attempts to reduce the redundancy in time-frequency (TF) feature have become an important subject [1][2][3]. Towards this end, some works [4][5][6] treat the TF spectrum as images.…”

Section: Introductionmentioning

confidence: 99%

Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar

et al. 2020

View full text Add to dashboard Cite

Time-frequency (TF) signal features are widely used in specific emitter identification (SEI) which commonly arises in many applications, especially for radar signals. Due to data scale and algorithm complexity, it is difficult to obtain an informative representation for SEI with existing TF features. In this paper, a feature extraction method is proposed based on synchrosqueezing transform (SST). The SST feature has an equivalent dimension to Fourier transform, and retains the most relevant information of the signal, leading to on average approximately 20 percent improvement in SEI for complex frequency modulation signals compared with existing handcrafted features. Numerous results demonstrate that the synchrosqueezing TF feature can offer a better recognition accuracy, especially for the signals with intricate time-varying information. Moreover, a linear relevance propagation algorithm is employed to attest to the SST feature importance from the perspective of deep learning.

show abstract

“…There are several methods for the classification of different radar signals based on the characteristics of the PDW or other distinctive features extracted from the signal such as features based on cumulant [2], empirical mode decomposition (EMD) [3] and time–frequency measures [4–8].…”

Section: Introductionmentioning

confidence: 99%

“…In [6], a method has been proposed based on the time–frequency distribution (TFD) and convolutional neural network. In [7], a new technique has been proposed for feature extraction of modulated signals which is based on a pattern recognition approach and Margenau‐Hill distribution. This method is robust against noise up to ∼10 dB SNR.…”

Section: Introductionmentioning

confidence: 99%

Radar signals classification using energy‐time‐frequency distribution features

Seddighi

Ahmadzadeh

Taban

2020

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

Classification of radar signals using time-frequency transforms and fuzzy clustering

Cited by 9 publications

References 5 publications

Radar signal recognition based on time-frequency representations and multidimensional probability density function estimator

Radar signal recognition based on time-frequency representations and multidimensional probability density function estimator

Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar

Radar signals classification using energy‐time‐frequency distribution features

Contact Info

Product

Resources

About